Understanding and predicting animal movements and distributions in the Anthropocene

Gomez, Sara, English, Holly M., Alegre, Vanesa Bejarano, Blackwell, Paul G., Bracken, Anna M., Bray, Eloise, Evans, Luke C., Gan, Jelaine L., Grecian, W. James, Gutmann Roberts, Catherine, Harju, Seth M., Hejcmanová, Pavla, Lelotte, Lucie, Marshall, Benjamin Michael, Matthiopoulos, Jason, Mnenge, AichiMkunde Josephat, Niebuhr, Bernardo Brandão, Ortega, Zaida, Pollock, Christopher J., Potts, Jonathan R., Russell, Charlie J. G., Rutz, Christian, Singh, Navinder J., Whyte, Katherine F. and Börger, Luca (2025) Understanding and predicting animal movements and distributions in the Anthropocene. Journal of Animal Ecology, 94 (6). ISSN 0021-8790

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Abstract

Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human- modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision- making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non- supervised use of citizen science data, AI and machine learning models

Item Type:	Article
Uncontrolled Keywords:	biologging,conservation,human-modified landscapes,modelling,movement ecology
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Depositing User:	LivePure Connector
Date Deposited:	25 Mar 2026 10:30
Last Modified:	29 Mar 2026 06:35
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102574
DOI:	10.1111/1365-2656.70040Digital

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